Els Goetghebeur

Comparing compliance patterns between randomized treatments

When two equally efficacious drugs enter the market, the one with the better compliance is likely to be more widely used. Special management of the delivery may produce increased compliance. In this paper we analyze a trial of a single drug dosing prescription with patients randomized to either daily self monitoring of the outcome (blood pressure) or not. The study used Medication Event Monitoring Systems (MEMS) to record each exact time and date when a patient opened the pill container. No established method is available for comparing these high-dimensional compliance patterns between groups. This paper investigates several summary measures that highlight different dimensions of the pattern and the drug context in which they may be meaningful. Further, we examine conditional and marginal models that enable comparisons of the full pattern of daily dosing indicators for subjects between the groups. We found no simple difference in average compliance levels, but we found an interesting interaction between treatment and time: similar compliance existed initially among patients in both randomized groups, with a stronger decline over time for patients who did not monitor their blood pressure. We discuss how a balance between simplicity of interpretation and efficiency of data use may be sought in this case.

Sensitivity analysis for incomplete contingency tables: the Slovenian plebiscite case

Classical inferential procedures induce conclusions from a set of data to a population of interest, accounting for the imprecision resulting from the stochastic component of the model. Less attention is devoted to the uncertainty arising from (unplanned) incompleteness in the data. Through the choice of an identifiable model for non-ignorable non-response, one narrows the possible data-generating mechanisms to the point where inference only suffers from imprecision. Some proposals have been made for assessing the sensitivity to these modelling assumptions; many are based on fitting several plausible but competing models. For example, we could assume that the missing data are missing at random in one model, and then fit an additional model where non-random missingness is assumed. On the basis of data from a Slovenian plebiscite, conducted in 1991, to prepare for independence, it is shown that such an ad hoc procedure may be misleading. We propose an approach which identifies and incorporates both sources of uncertainty in inference: imprecision due to finite sampling and ignorance due to incompleteness. A simple sensitivity analysis considers a finite set of plausible models. We take this idea one step further by considering more degrees of freedom than the data support. This produces sets of estimates (regions of ignorance) and sets of confidence regions (combined into regions of uncertainty).

A causal proportional hazards estimator for the effect of treatment actually received in a randomized trial with all-or-nothing compliance.

Survival data from randomized trials are most often analyzed in a proportional hazards (PH) framework that follows the intention-to-treat (ITT) principle. When not all the patients on the experimental arm actually receive the assigned treatment, the ITT-estimator mixes its effect on treatment compliers with its absence of effect on noncompliers. The structural accelerated failure time (SAFT) models of Robins and Tsiatis are designed to consistently estimate causal effects on the treated, without direct assumptions about the compliance selection mechanism. The traditional PH-model, however, has not yet led to such causal interpretation. In this article, we examine a PH-model of treatment effect on the treated subgroup. While potential treatment compliance is unobserved in the control arm, we derive an estimating equation for the Compliers PROPortional Hazards Effect of Treatment (C-PROPHET). The jackknife is used for bias correction and variance estimation. The method is applied to data from a recently finished clinical trial in cancer patients with liver metastases.

Vaginal lavage with chlorhexidine during labour to reduce mother-to-child HIV transmission: clinical trial in Mombasa Kenya.

ObjectivesTo evaluate the effect of vaginal lavage with diluted chlorhexidine on mother-to child transmission of HIV (MTCT) in a breastfeeding population. MethodsThis prospective clinical trial was conducted in a governmental hospital in Mombasa, Kenya. On alternating weeks, women were allocated to non-intervention or to intervention consisting of vaginal lavage with 120 ml 0.2% chlorhexidine, later increased to 0.4%, repeated every 3 h from admission to delivery. Infants were tested for HIV by DNA polymerase chain reaction within 48 h and at 6 and 14 weeks of life. ResultsEnrolment and follow-up data were available for 297 and 309 HIV-positive women, respectively, in the non-lavage and the lavage groups. There was no evidence of a difference in intrapartum MTCT (17.2 versus 15.9%, OR 0.9, 95% CI 0.6–1.4) between the groups. Lavage solely before rupture of the membranes tended towards lower MTCT with chlorhexidine 0.2% (OR O.6, 95% CI 0.3–1.1), and even more with chlorhexidine 0.4% (OR 0.1, 95% CI 0.0–0.9). ConclusionThe need remains for interventions reducing MTCT without HIV testing, often unavailable in countries with a high prevalence of HIV. Vaginal lavage with diluted chlorhexidine during delivery did not show a global effect on MTCT in our study. However, the data suggest that lavage before the membranes are ruptured might be associated with a reduction of MTCT, especially with higher concentrations of chlorhexidine.

Preterm birth in twins after subfertility treatment: population based cohort study

Abstract Objectives To assess gestational length and prevalence of preterm birth among medically and naturally conceived twins; to establish the role of zygosity and chorionicity in assessing gestational length in twins born after subfertility treatment. Design Population based cohort study. Setting Collaborative network of 19 maternity facilities in East Flanders, Belgium (East Flanders prospective twin survey). Participants 4368 twin pairs born between 1976 and 2002, including 2915 spontaneous twin pairs, 710 twin pairs born after ovarian stimulation, and 743 twin pairs born after in vitro fertilisation or intracytoplasmic sperm injection. Main outcome measures Gestational length and prevalence of preterm birth. Results Compared with naturally conceived twins, twins resulting from subfertility treatment had on average a slightly decreased gestational age at birth (mean difference 4.0 days, 95% confidence interval 2.7 to 5.2), corresponding to an odds ratio of 1.6 (1.4 to 1.8) for preterm birth, albeit confined to mild preterm birth (34-36 weeks). The adjusted odds ratios of preterm birth after subfertility treatment were 1.3 (1.1 to 1.5) when controlled for birth year, maternal age, and parity and 1.6 (1.3 to 1.8) with additional control for fetal sex, caesarean section, zygosity, and chorionicity. Although an increased risk of preterm birth was therefore seen among twins resulting from subfertility treatment, the risk was largely caused by a first birth effect among subfertile couples; conversely, the risk of prematurity was substantially levelled off by the protective effect of dizygotic twinning. Conclusions Twins resulting from subfertility treatment have an increased risk of preterm birth, but the risk is limited to mild preterm birth, primarily by virtue of dizygotic twinning.

The Ghent Marfan Trial — A randomized, double-blind placebo controlled trial with losartan in Marfan patients treated with β-blockers

BACKGROUND Aortic root dilation, dissection and rupture are major clinical problems in Marfan syndrome (MFS). Although β-blockers remain the standard of preventive treatment, preliminary results from animal studies and a selected group of severely affected MFS children show significant benefit from treatment with losartan, an angiotensin II receptor blocker with TGF-β inhibiting potential. Large-scale human trials are now needed to confirm these results. This trial aims to evaluate the combined effect of both drugs. METHODS We are conducting a prospective randomized placebo controlled double blind phase III study aiming to include 174 MFS patients (age ≥ 10 years and z-score ≥ 2). Patients already taking β-blockers are randomized for weight-adjusted treatment with losartan versus placebo. The primary endpoint is decrease in aortic root growth rate. Secondary endpoints are aortic dissection/surgery, progression of aortic/mitral regurgitation, arterial stiffness, left ventricular systolic/diastolic function, quality of life and genetic modifiers. Echocardiography, vascular echo-Doppler and quality of life assessment will be performed at baseline and at 6-monthly follow-ups for 3 years. MRI evaluation will be performed at baseline and at the end of the trial. CONCLUSION This trial will study new therapeutic strategies for the prevention of serious cardiovascular complications in MFS. The uniqueness in our trial is that the additive effect of losartan and β-blocker will be evaluated in a large spectrum of disease severity. A combination of ultrasound and MRI will allow detailed evaluation of anatomic and functional properties of the aorta and left ventricle.

Adherence and its measurement in phase 2/3 microbicide trials.

Adherence optimization and measurement have emerged as critically challenging issues for clinical trials of topical microbicides. Although microbicide trials have routinely collected adherence data, their utilization in trial design, implementation, and interpretation has been inconsistent. Drawing on data-driven presentations from several focused meetings, this paper synthesizes lessons from past microbicide trials and provides recommendations for future trials of microbicide and other HIV prevention technologies. First, it describes four purposes for adherence data collection, with particular attention to intention-to-treat versus adherence-adjusted analyses for determining effectiveness. Second, the microbicide field’s experiences with adherence measures and data collection modes are discussed, including the strengths and weaknesses of various options and approaches for improving measurement. Then, several approaches to optimizing trial participants’ adherence are presented. The paper concludes with a set of recommendations for immediate use or further research.

Nonrandom missingness in categorical data: strengths and limitations

Abstract There have recently been substantial developments in the analysis of incomplete data. Modeling tools are now available for nonrandom missingness and these methods are finding their way into the broad statistical community. The computational and interpretational issues that surround such models are less well known. This article provides an exposition of several of these issues in a categorical data setting. It is argued that the use of contextual information can aid the modeler in discriminating among models that are indistinguishable purely on statistical grounds.

The impact of compliance in pharmacokinetic studies.

In population pharmacokinetic (PK) studies, one observes just a few concentration measures spread out in time, on a sizable sample of the target population. Common-sense dictates that for estimation of a drug exposure-plasma concentration relationship, one needs accurate information on drug intake history besides the concentration measures. The population PK literature is well aware of this. Studies of simulated compliance behaviour have helped quantify the problem with naive compliance estimators and pointed towards a solution. In this paper we look at actually observed compliance patterns recorded via electronic monitoring. We simulate a documented pharmacokinetic model from the hypertensive literature on top of these and come to some interesting findings. In this clinical trial the problem of noncompliance is much more dramatic than simulated compliance patterns suggested so far. The systematic errors made by compliance naive estimators can be corrected when using timing explicit hierarchical nonlinear models and accurate information on a number of previous dose timings. When it is possible to observe irregular drug intake times in a well-controlled study, a substantial amount of precision is retrieved from the same number of data points. In general, the estimators of PK parameters benefit greatly from information that enters through greater variation in the drug-exposure process. Here we find support for the claim that noncompliance as a rich natural experiment of dosing variation can be a blessing rather than a curse from the information/learning point of view.

On Instrumental Variables Estimation of Causal Odds Ratios

Inference for causal effects can benefit from the availability of an instrumental variable (IV) which, by definition, is associated with the given exposure, but not with the outcome of interest other than through a causal exposure effect. Estimation methods for instrumental variables are now well established for continuous outcomes, but much less so for dichotomous outcomes. In this article we review IV estimation of so-called conditional causal odds ratios which express the effect of an arbitrary exposure on a dichotomous outcome conditional on the exposure level, instrumental variable and measured covariates. In addition, we propose IV estimators of so-called marginal causal odds ratios which express the effect of an arbitrary exposure on a dichotomous outcome at the population level, and are therefore of greater public health relevance. We explore interconnections between the different estimators and support the results with extensive simulation studies and three applications.